AWS Data Engineer

Cynet Systems•Charlotte, NC

20h

About The Position

The AWS Data Engineer designs and optimizes scalable data pipelines and processing workflows using Python, PySpark, and SQL. The role also develops and maintains production-grade APIs and backend services using Java and Python, with emphasis on API design, integration, performance, and reliability. It uses AWS services including S3, Glue, EMR, Lambda, and Redshift to build and manage cloud-based data engineering solutions. The position works with Model Context Protocol (MCP), large language models (LLMs), and generative AI to develop or integrate AI-enabled applications and workflows. It applies object-oriented programming principles, design patterns, and engineering best practices to produce reusable, maintainable, scalable code; optimizes SQL queries and ETL/ELT processes; and collaborates across technical and business teams. The engineer also troubleshoots APIs, pipelines, integrations, and cloud applications, and participates in code reviews, testing, CI/CD, performance tuning, and production support.

Requirements

Hands-on development experience in data engineering, backend development, or software engineering.
Strong hands-on experience with AWS data and cloud services, including S3, Glue, EMR, Lambda, and Redshift.
Experience developing and maintaining production-grade APIs and backend services using Java.
Strong understanding of RESTful API design, integration patterns, and error handling.
Strong Python programming skills, including experience building reusable modules, backend components, or API integrations.
Hands-on experience with PySpark and distributed data processing, including scalable data transformations and data-pipeline optimization.
Proficiency in SQL, including complex queries, data transformations, and query-performance optimization.
Strong understanding of object-oriented programming, software design principles, data structures, and maintainable application architecture.
Practical exposure to MCP, LLMs, generative AI, or AI application integration, and understanding of integrating AI capabilities into APIs and enterprise workflows.
Experience with API testing, debugging, authentication and authorization, and application integration.
Familiarity with Git, CI/CD pipelines, automated testing, and Agile development practices.
Strong problem-solving, communication, and collaboration skills, with the ability to work independently on complex technical requirements.

Nice To Haves

Experience building AI-enabled applications, LLM-powered workflows, or MCP-based integrations.
Exposure to LLM orchestration frameworks, tool calling, retrieval-augmented generation (RAG), or enterprise AI integration patterns.
Experience with AWS-native AI services and deployment of AI-enabled applications in cloud environments.
Familiarity with API gateways, containerization, microservices architecture, and application observability.
Experience with data warehousing, distributed computing, Spark performance tuning, and data quality frameworks.

Responsibilities

Design, develop, and optimize scalable data pipelines and data-processing workflows using Python, PySpark, and SQL.
Develop, enhance, and maintain production-grade APIs and backend services using Java and Python, focusing on API design, integration, performance, and reliability.
Build and integrate RESTful APIs and microservices for data-driven applications and enterprise integrations.
Use AWS services such as S3, Glue, EMR, Lambda, and Redshift to build and manage scalable data engineering solutions.
Work with MCP, LLMs, and generative AI technologies to develop or integrate AI-enabled applications and workflows.
Implement reusable, maintainable, and scalable code using object-oriented programming principles, design patterns, and engineering best practices.
Write and optimize complex SQL queries and implement efficient ETL/ELT processes for large-scale data transformations.
Collaborate with data engineers, application developers, architects, data scientists, and business stakeholders to translate requirements into technical solutions.
Troubleshoot and resolve issues across APIs, data pipelines, integrations, and cloud-based applications.
Participate in code reviews, automated testing, CI/CD implementation, performance tuning, and production support while adhering to engineering standards.